2016 Colorado Financial Contributions

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Basic Data

## 'data.frame':    164206 obs. of  20 variables:
##  $ cmte_id          : chr  "C00575795" "C00577130" "C00574624" "C00574624" ...
##  $ cand_id          : Factor w/ 25 levels "P00003392","P20002671",..: 1 12 10 10 10 12 10 12 12 12 ...
##  $ cand_nm          : Factor w/ 25 levels "Bush, Jeb","Carson, Benjamin S.",..: 4 20 5 5 5 20 5 20 20 20 ...
##  $ contbr_nm        : Factor w/ 34113 levels ";HEIMAN, JAMES",..: 28580 17750 14126 14126 14126 17767 14126 17928 18142 18142 ...
##  $ contbr_city      : Factor w/ 616 levels ""," SUPERIOR",..: 32 79 226 226 226 199 226 233 199 199 ...
##  $ contbr_st        : Factor w/ 1 level "CO": 1 1 1 1 1 1 1 1 1 1 ...
##  $ contbr_zip       : Factor w/ 20491 levels "","00124","00125",..: 815 1386 13591 13591 13591 18622 13591 14231 18642 18642 ...
##  $ contbr_employer  : Factor w/ 10432 levels "","-","--NA",..: 8093 9630 5408 5408 5408 7145 5408 6548 6548 6548 ...
##  $ contbr_occupation: Factor w/ 6000 levels "","'CALL CENTER AGEN",..: 5497 5234 1722 1722 1722 1670 1722 3427 3427 3427 ...
##  $ contb_receipt_amt: num  100 50 50 100 100 15 100 15 10 10 ...
##  $ contb_receipt_dt : Factor w/ 632 levels "01-APR-15","01-APR-16",..: 2 94 22 267 476 94 600 114 94 114 ...
##  $ receipt_desc     : Factor w/ 24 levels "","* EARMARKED CONTRIBUTION: SEE BELOW REATTRIBUTION/REFUND PENDING",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ memo_cd          : Factor w/ 2 levels "","X": 2 1 1 1 1 1 1 1 1 1 ...
##  $ memo_text        : Factor w/ 95 levels "","$0.83 REFUNDED ON 10/25/2016",..: 19 14 1 1 1 14 1 14 14 14 ...
##  $ form_tp          : Factor w/ 3 levels "SA17A","SA18",..: 2 1 1 1 1 1 1 1 1 1 ...
##  $ file_num         : int  1091718 1077404 1077664 1077664 1077664 1077404 1077664 1077404 1077404 1077404 ...
##  $ tran_id          : Factor w/ 163919 levels "A001497D386DA4906B87",..: 48653 129137 92336 93516 94136 129425 94739 129673 129350 129609 ...
##  $ election_tp      : Factor w/ 4 levels "","G2016","P2016",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ zip              : chr  "80014" "80020" "80504" "80504" ...
##  $ ZCTA_USE         : chr  "80014" "80020" "80504" "80504" ...

We have 164206 observations of 20 variables.

Let’s add party so we can roll the data up a bit, the republican field was busy in 2016. The idea for party logic here: http://stackoverflow.com/questions/4622060/case-statement-equivalent-in-r

As we don’t have age data, I’d like to be able to compare retirees:non-retirees; adding a variable for retiree status.

##     cmte_id   cand_id                   cand_nm           contbr_nm
## 1 C00575795 P00003392   Clinton, Hillary Rodham        SMITH, KERRI
## 2 C00577130 P60007168          Sanders, Bernard  LEONAWICZ, MATTHEW
## 3 C00574624 P60006111 Cruz, Rafael Edward 'Ted' HOWELL, DIRK E. MR.
## 4 C00574624 P60006111 Cruz, Rafael Edward 'Ted' HOWELL, DIRK E. MR.
## 5 C00574624 P60006111 Cruz, Rafael Edward 'Ted' HOWELL, DIRK E. MR.
## 6 C00577130 P60007168          Sanders, Bernard       LERNER, LUKAS
##   contbr_city contbr_st contbr_zip                contbr_employer
## 1      AURORA        CO  800144038                  SELF-EMPLOYED
## 2  BROOMFIELD        CO  800209751 UNIVERSITY OF ALASKA FAIRBANKS
## 3   FIRESTONE        CO  805043508                LGS INNOVATIONS
## 4   FIRESTONE        CO  805043508                LGS INNOVATIONS
## 5   FIRESTONE        CO  805043508                LGS INNOVATIONS
## 6     DURANGO        CO  813014721            POWERHOUSE ELECTRIC
##    contbr_occupation contb_receipt_amt contb_receipt_dt receipt_desc
## 1 TEMPORARY STAFFING               100        01-APR-16             
## 2       STATISTICIAN                50        05-MAR-16             
## 3           ENGINEER                50        02-APR-16             
## 4           ENGINEER               100        14-APR-16             
## 5           ENGINEER               100        24-APR-16             
## 6        ELECTRICIAN                15        05-MAR-16             
##   memo_cd                           memo_text form_tp file_num
## 1       X              * HILLARY VICTORY FUND    SA18  1091718
## 2         * EARMARKED CONTRIBUTION: SEE BELOW   SA17A  1077404
## 3                                               SA17A  1077664
## 4                                               SA17A  1077664
## 5                                               SA17A  1077664
## 6         * EARMARKED CONTRIBUTION: SEE BELOW   SA17A  1077404
##         tran_id election_tp   zip ZCTA_USE cand_party retiree_status
## 1      C4666313       P2016 80014    80014   democrat    not_retired
## 2   VPF7BKWYW45       P2016 80020    80020   democrat    not_retired
## 3 SA17A.1536976       P2016 80504    80504 republican    not_retired
## 4 SA17A.1663942       P2016 80504    80504 republican    not_retired
## 5 SA17A.1715938       P2016 80504    80504 republican    not_retired
## 6   VPF7BKY1A44       P2016 81301    81301   democrat    not_retired

Univariate Plots Section

That’s a lot of receipts with a 0 amount. Exclude the top 1% of contributions to zoom in a bit.

Are there zip codes more likely to contribute?

Too specific. Are there cities more likely to contribute?

Are there occupations that are more likely to contribute?

The largest groups are retirees. I did not expect retirees to be donating so actively. Which candidate received more donations? I’ve heard Colorado is fairly purple.

Let’s check that by party

Histogram is super skewed, trying a pie chart.

Univariate Analysis

##    cmte_id               cand_id                           cand_nm     
##  Length:164206      P00003392:70843   Clinton, Hillary Rodham  :70843  
##  Class :character   P60007168:53689   Sanders, Bernard         :53689  
##  Mode  :character   P80001571:13718   Trump, Donald J.         :13718  
##                     P60006111:12953   Cruz, Rafael Edward 'Ted':12953  
##                     P60005915: 7112   Carson, Benjamin S.      : 7112  
##                     P60006723: 2201   Rubio, Marco             : 2201  
##                     (Other)  : 3690   (Other)                  : 3690  
##                contbr_nm                contbr_city    contbr_st  
##  LENELL, MATT       :   390   DENVER          :32215   CO:164206  
##  IMMASCHE, SONIA    :   356   BOULDER         :14936              
##  SMITH, PHILIP      :   320   COLORADO SPRINGS:11039              
##  CASPERSON, CAROLINA:   304   FORT COLLINS    : 7494              
##  RAMSEY, WILLIAM    :   261   AURORA          : 6109              
##  HOFFMAN, TONI      :   240   LITTLETON       : 5461              
##  (Other)            :162335   (Other)         :86952              
##      contbr_zip          contbr_employer              contbr_occupation
##  804212057:   390   N/A          :23483   RETIRED              :38933  
##  805241517:   356   RETIRED      :20878   NOT EMPLOYED         :16446  
##  802212506:   322   SELF-EMPLOYED:12233   INFORMATION REQUESTED: 3861  
##  802034502:   304   NONE         :12047   ATTORNEY             : 3229  
##  814015616:   300   NOT EMPLOYED : 6761   TEACHER              : 2536  
##  80504    :   245   (Other)      :88626   (Other)              :99169  
##  (Other)  :162289   NA's         :  178   NA's                 :   32  
##  contb_receipt_amt  contb_receipt_dt    
##  Min.   :-16300.0   Min.   :2014-08-16  
##  1st Qu.:    15.0   1st Qu.:2016-02-28  
##  Median :    27.0   Median :2016-05-06  
##  Mean   :   102.6   Mean   :2016-05-11  
##  3rd Qu.:    75.0   3rd Qu.:2016-08-18  
##  Max.   : 18000.0   Max.   :2016-11-27  
##                                         
##                            receipt_desc    memo_cd   
##                                  :162356    :139603  
##  Refund                          :   883   X: 24603  
##  REDESIGNATION FROM PRIMARY      :   232             
##  REDESIGNATION TO GENERAL        :   225             
##  REDESIGNATION TO CRUZ FOR SENATE:   144             
##  SEE REDESIGNATION               :   116             
##  (Other)                         :   250             
##                                memo_text      form_tp      
##                                     :98139   SA17A:139760  
##  * EARMARKED CONTRIBUTION: SEE BELOW:52328   SA18 : 23563  
##  * HILLARY VICTORY FUND             :12312   SB28A:   883  
##  REDESIGNATION FROM PRIMARY         :  232                 
##  REDESIGNATION TO GENERAL           :  225                 
##  EARMARKED FROM MAKE DC LISTEN      :  163                 
##  (Other)                            :  807                 
##     file_num            tran_id       election_tp        zip           
##  Min.   :1003942   C10182166:     2        :   400   Length:164206     
##  1st Qu.:1077648   C10233294:     2   G2016: 52017   Class :character  
##  Median :1093618   C10233584:     2   P2016:111788   Mode  :character  
##  Mean   :1095013   C10248517:     2   P2020:     1                     
##  3rd Qu.:1119042   C10258303:     2                                    
##  Max.   :1134173   C10262748:     2                                    
##                    (Other)  :164194                                    
##    ZCTA_USE               cand_party     retiree_status    
##  Length:164206      democrat   :124675   Length:164206     
##  Class :character   republican : 38751   Class :character  
##  Mode  :character   libertarian:   502   Mode  :character  
##                     green      :   219                     
##                     independent:    59                     
##                                                            
## 

Some of these people were contributing early and often. I’ll compare those to the 2015-2016 contribution limits here later: http://www.fec.gov/info/contriblimitschart1516.pdf

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -16300.0     15.0     27.0    102.6     75.0  18000.0

The histogram on contributions had a large group around zero – are there a large number of zero contributions?

## [1] 5

Not a lot of zero contributions, interesting. How long did the donation season extend?
There was some news speculation that large donors were waiting for the primaries to thin out.

##         Min.      1st Qu.       Median         Mean      3rd Qu. 
## "2014-08-16" "2016-02-28" "2016-05-06" "2016-05-11" "2016-08-18" 
##         Max. 
## "2016-11-27"

What about the memos on the donations? That may have some interesting information.

## # A tibble: 95 × 2
##                                  memo_text     n
##                                     <fctr> <int>
## 1                                          98139
## 2      * EARMARKED CONTRIBUTION: SEE BELOW 52328
## 3                   * HILLARY VICTORY FUND 12312
## 4               REDESIGNATION FROM PRIMARY   232
## 5                 REDESIGNATION TO GENERAL   225
## 6            EARMARKED FROM MAKE DC LISTEN   163
## 7         REDESIGNATION TO CRUZ FOR SENATE   144
## 8                     *BEST EFFORTS UPDATE   135
## 9                        SEE REDESIGNATION   116
## 10 REATTRIBUTION / REDESIGNATION REQUESTED    69
## # ... with 85 more rows

What is the structure of your dataset?

normalized, there’s one entry per donation (or refund)

What is/are the main feature(s) of interest in your dataset?

We could roll up to see how much each candidate received as a whole, or how much specific earmarks received, and drill down to see if someone in the state went over a contribution limit.

What other features in the dataset do you think will help support your investigation into your feature(s) of interest?

Each candidate is ID’d, and eafch contributor is specifically named, along with the date of hteir contribution and the amount of their contribution. Any memos with specific earmarks or refund memos are designated clearly.

Did you create any new variables from existing variables in the dataset?

No.

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?

I transformed the date into date format to make summary analysis simpler on that variable. There were a lot of donations that seemed centered around zero. I did exclude the top 1% while looking at these so I could see the data a bit closer, there are only 5 actual zero contributions. The others in the near-zero range appear to be micro-transactions and not actually zero.

Bivariate Plots Section

Did the giving patterns (many small donations, few large donations) vary across candidates? Using log10 because these are financial amounts, and diff candidates (naturally) received very diff amounts in this state. I’m not interested in how much total they received, but what that distro looks like. Roll that up by party, keep log scale as total coming into various parties has lg difference.

same idea, different angle – $ by candidate

That’s beyond busy and not very useful. Excluding refunds, let’s look at donations by party (also excluding top 1%) Do specific parties get more high-dollar donations?

Did larger/smaller donations come in at different points in the election cycle? Using ylim to exclude some outliers Which party earmarked more funds? I’ve heard there’s a strong rural/urban funding divide – visible in this data? This seems to reflect the histogram of cities showing that most of the money came from urban.

Bivariate Analysis

There were several misc refunds in the memo data in uni-variate

## # A tibble: 35 × 3
##                                                           memo_text     n
##                                                              <fctr> <int>
## 1  * EARMARKED CONTRIBUTION: SEE BELOW REATTRIBUTION/REFUND PENDING    23
## 2                                            REFUNDED ON 10/24/2016     9
## 3                                               REFUND TO BE ISSUED     6
## 4        * EARMARKED CONTRIBUTION: SEE BELOW REFUNDED ON 10/24/2016     3
## 5                                            REFUNDED ON 10/10/2016     3
## 6                                      REATTRIBUTION/REFUND PENDING     2
## 7                                   REFUNDED $1000.00 ON 12/29/2015     2
## 8                                            REFUNDED ON 10/18/2016     2
## 9                                             REFUNDED ON 7/12/2016     2
## 10                                     $0.83 REFUNDED ON 10/25/2016     1
## # ... with 25 more rows, and 1 more variables: meanrefund <dbl>

How different were the average amounts for each candidate? The story in the news was that the democratic candidates were receiving more money through more frequent, smaller donations.

## # A tibble: 25 × 6
##                      cand_nm total_donation mean_donation median_donation
##                       <fctr>          <dbl>         <dbl>           <dbl>
## 1    Clinton, Hillary Rodham      8544967.4     120.61837              25
## 2           Sanders, Bernard      2298527.4      42.81189              27
## 3           Trump, Donald J.      2456085.8     179.04110              60
## 4  Cruz, Rafael Edward 'Ted'      1011921.4      78.12255              50
## 5        Carson, Benjamin S.       694383.1      97.63541              50
## 6               Rubio, Marco       632342.2     287.29768             100
## 7             Fiorina, Carly       223509.6     236.26813             100
## 8                 Paul, Rand       105243.5     160.67716              50
## 9              Johnson, Gary       128571.1     256.11771             100
## 10                 Bush, Jeb       276847.0     692.11750             250
## # ... with 15 more rows, and 2 more variables: stdev_donation <dbl>,
## #   n <int>

Data in this reflects the news story! How different were average amounts by party? Since Dean’s presidential campaign, dems have supposedly had an edge on smaller/frequent donations.

## # A tibble: 5 × 5
##    cand_party total_donation mean_donation median_donation      n
##        <fctr>          <dbl>         <dbl>           <dbl>  <int>
## 1    democrat    10930322.95      87.67053              25 124675
## 2  republican     5762403.22     148.70334              50  38751
## 3 libertarian      128571.09     256.11771             100    502
## 4       green       21388.27      97.66333              50    219
## 5 independent       12156.00     206.03390             100     59

Donations by city? do rural areas contribute less to political donations due to high-paying jobs often being concentrated in urban areas?

## # A tibble: 616 × 5
##          contbr_city total_donation mean_donation median_donation     n
##               <fctr>          <dbl>         <dbl>           <dbl> <int>
## 1             DENVER      4190800.7     130.08849              27 32215
## 2            BOULDER      1752512.3     117.33478              27 14936
## 3   COLORADO SPRINGS      1018770.6      92.28830              30 11039
## 4       FORT COLLINS       520008.9      69.39003              25  7494
## 5          ENGLEWOOD       461519.4     192.54043              35  2397
## 6          LITTLETON       454958.3      83.31044              27  5461
## 7              ASPEN       432404.5     428.54759              40  1009
## 8             AURORA       376474.7      61.62624              25  6109
## 9  GREENWOOD VILLAGE       333703.3     324.93023              75  1027
## 10        CENTENNIAL       330993.5     100.94341              28  3279
## # ... with 606 more rows

First, I saw a story that reported researchers using national donation data to find people who donated more than the contribution limit for the 2015-2016 federal elections. Let’s see if that’s visible in this data.

## Source: local data frame [671 x 4]
## Groups: contbr_nm [668]
## 
##                contbr_nm                   cand_nm total_donation     n
##                   <fctr>                    <fctr>          <dbl> <int>
## 1           FARKAS, JOEL              Rubio, Marco          18900     7
## 2        JABS, JACOB MR.              Rubio, Marco          13500     5
## 3         BARRON, CURRIE   Clinton, Hillary Rodham          10800     4
## 4          DOUD, BEN MR.              Rubio, Marco          10800     4
## 5      FARKAS, DANA MRS.              Rubio, Marco          10800     4
## 6     SREDNICKI, RICHARD          Trump, Donald J.           9900     4
## 7  BARRON, THOMAS A. MR.        Graham, Lindsey O.           8100     6
## 8       CONGDON, NOEL R.   Clinton, Hillary Rodham           8100     3
## 9        CONGDON, THOMAS   Clinton, Hillary Rodham           8100     5
## 10        COORS, JEFFREY Cruz, Rafael Edward 'Ted'           8100     5
## # ... with 661 more rows

There’s a lot of donations above the individual to candidate max – hopefully these are PAC donations that were filed as ‘for’ a specific candidate.

News stories frequently reported an age difference between party donation. Is that reflected here?

## # A tibble: 10 × 6
##    retiree_status  cand_party total_donation mean_donation median_donation
##             <chr>      <fctr>          <dbl>         <dbl>           <dbl>
## 1     not_retired    democrat     8659734.69      88.92450            27.0
## 2     not_retired  republican     3794510.73     171.51106            50.0
## 3     not_retired libertarian      117956.59     262.12576           100.0
## 4     not_retired       green       15388.27      93.26224            50.0
## 5     not_retired independent        8288.50     184.18889           100.0
## 6         retired    democrat     2270588.26      83.19611            25.0
## 7         retired  republican     1967892.49     118.35523            50.0
## 8         retired libertarian       10614.50     204.12500           100.0
## 9         retired       green        6000.00     111.11111            50.0
## 10        retired independent        3867.50     276.25000            37.5
## # ... with 1 more variables: n <int>

There’s often an age split discussed in political news. Are retirees in Colorado more likely to be democrat or republican?

## [1] 0.2

Phi is close to zero, there is little or no association between retiree status and party in the Colorado donor data.

Were earmarked funds more likely to be higher amounts?

## # A tibble: 2 × 5
##   `if_else(grepl("EARMARK", memo_text), ...` total_donation mean_donation
##                                        <chr>          <dbl>         <dbl>
## 1                                 Earkmarked        2262311      43.06129
## 2                              Not Earmarked       14592530     130.67665
## # ... with 2 more variables: median_donation <dbl>, n <int>

Were Republicans/Democrats more likely to earmark funds?

## [1] 0.38

Phi is 0.38, a bit further from zero, weak positive association between party and earmarking funds in the Colorado donor data.

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?

I found it really interesting that the Democratic party did fundraise successfully using smaller, more frequent, donations than the Republican party. I had read articles and heard news stories alleging this was their tactic, and it’s interesting to see how that strategy was successful in Colorado.

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?

I was previously unaware of how many refunds existed in election fundraising.
It was interesting, but not incredibly surprising, to see the total funding by region mirror population density.

What was the strongest relationship you found?

A weak positive association between earmarks and democrats. Democrats were more likely to use earmarks. Combining this with the summarised earmark data, these were also for lower amounts than the average donation.

Multivariate Plots Section

Did some people contribute more frequently, and were their contribution totals higher than those who contributed less often? How do financial amounts vary for retirees vs non-retirees with political party?

Were different parties getting more for retirees vs non-retirees?

Was there a strong divide for rural/urban on party line funding? That wasn’t very visually helpful–let’s look at the spread between the two specifically Were larger funds earmarked? Interesting, it looks like smaller funds were more often earmarked.

Multivariate Analysis

Did the retiree/non-retiree donation split hard on party line on money, not amount?

## [1] 0.15

Phi is 0.15, little/no association between retiree status and total donation in the Colorado donor data. Is there a relationship between the mean?

## [1] -0.07

Phi is -0.07, that’s even closer to zero, so even a lower association between the mean amount donated and retiree status.

Is there a relationship between donation frequency & amount?

## 
## Calls:
## m1: lm(formula = I(total_donation) ~ I(number_of_donations), data = frequent_contributors)
## m2: lm(formula = I(total_donation) ~ I(number_of_donations) + cand_party, 
##     data = frequent_contributors)
## 
## ==================================================================
##                                          m1             m2        
## ------------------------------------------------------------------
##   (Intercept)                        393.599***      492.426***   
##                                       (5.170)         (7.283)     
##   I(number_of_donations)              17.367***       14.862***   
##                                       (0.513)         (0.527)     
##   cand_party: republican/democrat                   -183.081***   
##                                                       (9.498)     
##   cand_party: libertarian/democrat                   -33.675      
##                                                      (53.518)     
##   cand_party: green/democrat                        -273.951**    
##                                                      (94.900)     
##   cand_party: independent/democrat                  -116.455      
##                                                     (157.603)     
## ------------------------------------------------------------------
##   R-squared                                   0.0            0.0  
##   adj. R-squared                              0.0            0.0  
##   sigma                                     866.9          862.4  
##   F                                        1145.8          306.9  
##   p                                           0.0            0.0  
##   Log-likelihood                        -291157.0      -290969.7  
##   Deviance                          26736283722.0  26456217911.4  
##   AIC                                    582320.0       581953.4  
##   BIC                                    582345.5       582012.7  
##   N                                       35577          35577    
## ==================================================================

That is an awful model. I’m not seeing a predictible relationship between the variables here, I suspect there’s hidden motivators not in this data set.

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?

Donations mostly came from urban areas. Urban areas were also more likely to be donating Democratic. Combined, the urban areas were donating significantly more to the Democratic party.

Were there any interesting or surprising interactions between features?

It was also interesting that retirees were donating higher amounts to the Republican party. As a whole, retirees donated almost purple–the sum of democrat donations vs the sum of republican donations is very similar as a part of the total donations in Colorado.

OPTIONAL: Did you create any models with your dataset? Discuss the strengths and limitations of your model.

I created a model and was completely unable to predict where someone would donate based on

---

Final Plots and Summary

Plot One

Description One

The majority of moneys donated in the 2016 election from Colorado residents was for Democratic candidates; 10.93 million Democratic to 5.76 million Republican.

Plot Two

Description Two

The Democrat/Republican donation split for retirees in Colorado is almost purple, barely leaning Democratic. However, the non-retiree population’s donations are financially heavily Democratic. This fits in nicely with the local wisdom that the urban areas vote Democratic, while the prarie votes Republican.

Plot Three

Description Three

I overlaid the map to show how high population areas and ‘resort’ areas donated significantly more funds to Democratic candidates than Republican candidates in the 2016 election cycle. The N/A areas are areas with very low population. I’m using ZCTA instead of true Zip code because choropleth runs off ZCTA, as does census data. In this graph, the bluest areas are the areas with the highest funding difference in favor of the Democratic party.

Reflection

I validated several stories that had been trending in the news during the election cycle in this data set.

I often heard Colorado referred to as ‘purple’, leaning between Democratic and Republican, so I checked to see if the financial donations reflected that even split. This was not the case in the overall donation sums between the two parties.

In detail, I verified the news story that the Democratic funding strategy successfully raised more money by raising more frequent, smaller, donations while the Republican party went for larger, less frequent donations. On average, the Colorado donations were 50 dollars for Democrats and 100 dollars for republicans. In the violin chart, there’s a large visual difference between the large amount donations in the two parties.

I suspected the difference between the financials could be due to the differnce between older and younger contributors, as the millenial vote was strongly Democratic this election season. That was validated in the data–the split between retirees was almost even, while the split between non-retirees was rather high–8659734.69 dollars to 3794510.73 dollars.

Lastly, I’ve heard that the red/blue split is heavily influenced by geography, with Republican voters living in the prarie and Democratic voters living in the mountains and highly populated urban areas. This was validated in the data as seen in the difference between blue/Democratic fundraising and white/Republican fundraising in the choropleth map.